AI-generated reporting significantly reduces radiologists' reading times and increases report acceptability over time.
Key Details
- 1Study published in JACR evaluated AI-generated reports for chest X-rays.
- 2Researchers used Kakao Brain’s KARA-CXR model on a dataset of 756 radiographs.
- 3Five radiologists generated preliminary AI reports; two assessed acceptability and need for revisions.
- 4Human approval rates of AI-generated reports increased, indicating growing trust.
- 5Experts call for further study into improving preliminary AI reports and their impact on diagnostics.
Why It Matters
Streamlining reporting with AI could improve radiologist efficiency and address workforce pressures. Increased acceptability suggests AI integration is advancing, but accuracy and clinical impact remain focus areas for future research.
Source
Radiology Business
Related News
•AuntMinnie
AI Model Uses Ultrasound to Assess Fetal Lung Maturity
Researchers demonstrated an AI model's strong accuracy in measuring fetal lung maturity from ultrasound images.
•AuntMinnie
AI Model Predicts Dosimetry for Lu-177 PSMA Therapy Using PET/CT
A machine learning PET/CT model shows promise for predicting radiation dose prior to Lu-177 PSMA therapy in prostate cancer patients.
•AuntMinnie
LLM AI Significantly Boosts MRI Accuracy For Less Experienced Readers
AI LLMs notably improve diagnostic accuracy for less experienced brain MRI readers, with diminishing benefits for experts.